106 citations · 132 across the 6 of their papers we have counts for
8 papers
Deep Reinforcement Learning for Entity Alignment
Lingbing Guo, Yuqiang Han, Qiang Zhang +1
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The…
Unleashing the Power of Transformer for Graphs
Lingbing Guo, Qiang Zhang, Huajun Chen
Despite recent successes in natural language processing and computer vision, Transformer suffers from the scalability problem when dealing with graphs. The computational complexity…
Principled Representation Learning for Entity Alignment
Lingbing Guo, Zequn Sun, Mingyang Chen +3
Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of…
TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
Zequn Sun, Jiacheng Huang, Wei Hu +3
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping fu…
Multi-view Knowledge Graph Embedding for Entity Alignment
Qingheng Zhang, Zequn Sun, Wei Hu +3
We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorpora…
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
Lingbing Guo, Zequn Sun, Wei Hu
We study the problem of knowledge graph (KG) embedding. A widely-established assumption to this problem is that similar entities are likely to have similar relational roles. Howeve…